The automated real-time recognition of unexpected situations plays a crucial
role in the safety of autonomous vehicles, especially in unsupported and
unpredictable scenarios. This paper evaluates different Bayesian uncertainty
quantification methods from the deep learning domain for the anticipatory
testing of safety-critical misbehaviours during system-level simulation-based
testing. Specifically, we compute uncertainty scores as the vehicle executes,
following the intuition that high uncertainty scores are indicative of
unsupported runtime conditions that can be used to distinguish safe from
failure-inducing driving behaviors. In our study, we conducted an evaluation of
the effectiveness and computational overhead associated with two Bayesian
uncertainty quantification methods, namely MC- Dropout and Deep Ensembles, for
misbehaviour avoidance. Overall, for three benchmarks from the Udacity
simulator comprising both out-of-distribution and unsafe conditions introduced
via mutation testing, both methods successfully detected a high number of
out-of-bounds episodes providing early warnings several seconds in advance,
outperforming two state-of-the-art misbehaviour prediction methods based on
autoencoders and attention maps in terms of effectiveness and efficiency.
Notably, Deep Ensembles detected most misbehaviours without any false alarms
and did so even when employing a relatively small number of models, making them
computationally feasible for real-time detection. Our findings suggest that
incorporating uncertainty quantification methods is a viable approach for
building fail-safe mechanisms in deep neural network-based autonomous vehicles.

使用贝叶斯不确定性量化方法对深度学习领域的安全关键性行为进行评估，以实现在系统级仿真测试中对系统行为进行准确预测并提供及早的警告，研究发现深度集成方法能够有效并高效地检测并避免潜在的系统行为失控，为建立深度神经网络自主驾驶汽车的故障安全机制提供了可行的方法。